Fine-tuning large language models (LLMs) is a resource-intensive process. It demands significant memory and computational power, often making it impractical for researchers and developers with limited resources. But what if you could drastically reduce these requirements *while simultaneously boosting performance*? New research introduces AutoMixQ, a groundbreaking technique that achieves precisely this. AutoMixQ tackles the challenge of memory-efficient fine-tuning by intelligently combining three powerful methods: pruning, quantization, and Low-Rank Adaptation (LoRA). Pruning strategically removes less important parameters, shrinking the model's size. Quantization reduces the precision of numerical values, further decreasing memory footprint. LoRA fine-tunes the model by adding small, adaptable matrices instead of modifying the entire model's weights. The real magic of AutoMixQ lies in its ability to *self-adjust*. It dynamically determines the optimal quantization configuration for each layer of the LLM, adapting to the unique characteristics of the pruned model. This personalized approach, guided by lightweight performance models and Pareto optimality, ensures that resources are allocated efficiently, maximizing performance under strict memory constraints. Experiments on several LLMs, including LLaMA-7B and Vicuna-7B, have shown remarkable results. AutoMixQ consistently outperforms standard LoRA and LoftQ (quantized LoRA) in terms of both memory usage and accuracy, especially on tasks involving complex reasoning. With AutoMixQ, fine-tuning LLMs becomes far more accessible, enabling more researchers and developers to explore the exciting possibilities of these powerful models. This opens doors to deploying highly capable LLMs on resource-constrained devices, paving the way for broader access to cutting-edge AI technology.